Creating Bag-Of-Features.
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* Image category 1: books
* Image category 2: cups
* Selecting feature point locations using the Grid method.
* Extracting SURF features from the selected feature point locations.
** The GridStep is [8 8] and the BlockWidth is [32 64 96 128].

* Extracting features from 4 images...done. Extracted 76800 features.

* Keeping 80 percent of the strongest features from each category.

* Using K-Means clustering to create a 500 word visual vocabulary.
* Number of features          : 61440
* Number of clusters (K)      : 500

* Initializing cluster centers...100.00%.
* Clustering...completed 25/100 iterations (~0.17 seconds/iteration)...converged in 25 iterations.

* Finished creating Bag-Of-Features

Training an image category classifier for 2 categories.
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* Category 1: books
* Category 2: cups

* Encoding features for 4 images...done.

* Finished training the category classifier. Use evaluate to test the classifier on a test set.

Evaluating image category classifier for 2 categories.
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* Category 1: books
* Category 2: cups

* Evaluating 8 images...done.

* Finished evaluating all the test sets.

* The confusion matrix for this test set is:


             PREDICTED
KNOWN    | books   cups   
--------------------------
books    | 0.75    0.25   
cups     | 0.25    0.75   

* Average Accuracy is 0.75.

confMatrix = 2×2

    0.7500    0.2500
    0.2500    0.7500

ans = 0.7500

ans = 1x1 cell array
    {'cups'}